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R High Performance Programming

Book Image

R High Performance Programming

Overview of this book

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R High Performance Programming
Jan, 2015 | 176 pages
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Table of Contents (17 chapters)
R High Performance Programming
R High Performance Programming
Credits
Credits
About the Authors
About the Authors
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Understanding R's Performance – Why Are R Programs Sometimes Slow?
Understanding R's Performance – Why Are R Programs Sometimes Slow?
Three constraints on computing performance – CPU, RAM, and disk I/O
R is interpreted on the fly
R is single-threaded
R requires all data to be loaded into memory
Algorithm design affects time and space complexity
Summary
2
Profiling – Measuring Code's Performance
Profiling – Measuring Code's Performance
Measuring total execution time
Profiling the execution time
Profiling memory utilization
Monitoring memory utilization, CPU utilization, and disk I/O using OS tools
Identifying and resolving bottlenecks
Summary
3
Simple Tweaks to Make R Run Faster
Simple Tweaks to Make R Run Faster
Vectorization
Use of built-in functions
Preallocating memory
Use of simpler data structures
Use of hash tables for frequent lookups on large data
Seeking fast alternative packages in CRAN
Summary
4
Using Compiled Code for Greater Speed
Using Compiled Code for Greater Speed
Compiling R code before execution
Using compiled languages in R
Summary
5
Using GPUs to Run R Even Faster
Using GPUs to Run R Even Faster
General purpose computing on GPUs
R and GPUs
Fast statistical modeling in R with gputools
Summary
6
Simple Tweaks to Use Less RAM
Simple Tweaks to Use Less RAM
Reusing objects without taking up more memory
Removing intermediate data when it is no longer needed
Calculating values on the fly instead of storing them persistently
Swapping active and nonactive data
Summary
7
Processing Large Datasets with Limited RAM
Processing Large Datasets with Limited RAM
Using memory-efficient data structures
Using memory-mapped files and processing data in chunks
Summary
8
Multiplying Performance with Parallel Computing
Multiplying Performance with Parallel Computing
Data parallelism versus task parallelism
Implementing data parallel algorithms
Implementing task parallel algorithms
Executing tasks in parallel on a cluster of computers
Shared memory versus distributed memory parallelism
Optimizing parallel performance
Summary
9
Offloading Data Processing to Database Systems
Offloading Data Processing to Database Systems
Extracting data into R versus processing data in a database
Preprocessing data in a relational database using SQL
Converting R expressions to SQL
Running statistical and machine learning algorithms in a database
Using columnar databases for improved performance
Using array databases for maximum scientific-computing performance
Summary
10
R and Big Data
R and Big Data
Understanding Hadoop
Setting up Hadoop on Amazon Web Services
Processing large datasets in batches using Hadoop
Summary
Index
Index

Summary

In this chapter, we learned how to speed certain computations in R by leveraging GPUs. Given that most computers today come with a GPU, this gives a quick opportunity to improve the performance of R programs. This is especially true with the growing number of packages that interface R with GPUs. Some, such as gputools, require no knowledge of CUDA or OpenCL at all. GPUs do not guarantee improved performance for all tasks.

In the next chapter, we will turn our attention to address RAM-related issues in R programs.

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